A Comparison of Different Decision Algorithms Used in Volumetric Storm Cells Classification

Issue title: Concurrency Specification and Programming Workshop (CS&P'2001)

Article type: Research Article

Authors: Suraj, Zbigniew | Peters, J.F. | Rzasa, W.

Affiliations: Chair of Foundations of Computer Science, University of Information Technology and Management H. Sucharskiego 2, 35-225 Rzeszow, Poland | Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg MB R3T 5V6, Canada | Institute of Mathematics, University of Rzeszow, Rejtana 16A, 35-310 Rzeszow, Poland

Abstract: Decision algorithms useful in classifying meteorological volumetric radar data are the subject of described in the paper experiments. Such data come from the Radar Decision Support System (RDSS) database of Environment Canada and concern summer storms created in this country. Some research groups used the data completed by RDSS for verifying the utility of chosen methods in volumetric storm cells classification. The paper consists of a review of experiments that were made on the data from RDSS database of Environment Canada and presents the quality of particular classifiers. The classification accuracy coefficient is used to express the quality. For five research groups that led their experiments in a similar way it was possible to compare received outputs. Experiments showed that the Support Vector Machine (SVM) method and rough set algorithms which use object oriented reducts for rule generation to classify volumetric storm data perform better than other classifiers.

Keywords: rough sets, cross-validation, data mining, knowledge discovery, meteorological volumetric radar data, pattern recognition

Journal: Fundamenta Informaticae, vol. 51, no. 1-2, pp. 201-214, 2002